code: def test_regression_cv_2_and_mse(load_diabetes_dataset):
      #  test for regression using cv=2, and the neg_mean_squared_error as metric.
      # add suitable threshold for regression mse
  
      X, y = load_diabetes_dataset
      sel = SelectBySingleFeaturePerformance(
          estimator=DecisionTreeRegressor(random_state=0),
          scoring="neg_mean_squared_error",
          cv=2,
          threshold=-6000,
      )
      # fit transformer
      sel.fit(X, y)
  
      # expected output
      Xtransformed = X.copy()
      Xtransformed = Xtransformed[[1, 7]].copy()
  
      # test init params
      assert sel.cv == 2
      assert sel.scoring == "neg_mean_squared_error"
      assert sel.threshold == -6000
      # fit params
      assert sel.features_to_drop_ == [0, 2, 3, 4, 5, 6, 8, 9]
      rounded_perfs = {
          key: round(sel.feature_performance_[key], 2)
          for key in sel.feature_performance_
      }
      assert rounded_perfs == {
          0: -7657.15,
          1: -5966.66,
          2: -6613.78,
          3: -6488.93,
          4: -9415.59,
          5: -11761.0,
          6: -6592.58,
          7: -5270.56,
          8: -7547.2,
          9: -6287.56,
      }
      # test transform output
      print(sel.transform(X))
      pd.testing.assert_frame_equal(sel.transform(X), Xtransformed)
  
file_path: feature_engine/tests/test_selection/test_single_feature_performance_selection.py
imports: import warnings
  import numpy
  import pandas
  import pytest
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.linear_model import LinearRegression
  from sklearn.tree import DecisionTreeRegressor
  from feature_engine.selection import SelectBySingleFeaturePerformance
label_desc: Use image node_id 3 for calling a global function with example usage: test_regression_cv_2_and_mse(load_diabetes_dataset) without return types